With the rapid growth of the internet and the users of the internet over the past ten years, and the rapid increase in the amount of information available over the internet, a need for special tools for data/text/image/sequence of images/sounds search was developed. Many search engines are available to users and provide powerful tools for image search. Search engines propose different strategies from one another in attempting to find images which are most relevant to the user-specified search criteria. For example, one can define size of image (any size, extra-large, large, medium, small), type of image (any type, news, face, clipart, line drawings, photo), color (all colors, red, green, black, etc.).
Most of the known image search engines attempt to receive relevant documents by filtering, wherein an interface is provided to allow the user to set parameters to arrive at a set of relevant documents.
Some web-based search engines use data mining capabilities. Such capabilities may include clustering of images to groups by similar topics, which enables a search for the “nearest” results or for “similar” images. The clustering procedure may employ a group-average-linkage technique to determine relative affinity between documents. Additionally, clustering procedures may take into account behavior of similar users in the past. These clustering procedures usually use off-line “profile-oriented” or “history-oriented” learning systems. Additionally, some of these systems perform image search based on corresponding text label associated with each image.
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